下图为NeutralCF的模型结构图，总共两个分支，第一个分支为GML，第二个为MLP，GML通路将两个特征的Embedding向量进行内积操作，MLP将两个特征的Embedding的向量进行拼接，然后使用多层感知机进行传播，然后将两个通路输出的向量进行拼接，导入全连接层（输出层），输出Score。
一、导包
import tensorflow as tf from tensorflow.keras.layers import * from tensorflow.keras.models import * from tensorflow.keras.utils import plot_model from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler, LabelEncoder import itertools import pandas as pd import numpy as np from tqdm import tqdm from collections import namedtuple import warnings warnings.filterwarnings("ignore")
二、读取数据
# 读取数据，NCF使用的特征只有user_id和item_id rnames = ['user_id','movie_id','rating','timestamp'] data = pd.read_csv('./data/ml-1m/ratings.dat', sep='::', engine='python', names=rnames)
三、特征编码处理
lbe = LabelEncoder() data['user_id'] = lbe.fit_transform(data['user_id']) data['movie_id'] = lbe.fit_transform(data['movie_id']) train_data = data[['user_id', 'movie_id']] train_data['label'] = data['rating']
四、使用具名元组为特征进行处理
SparseFeat = namedtuple('SparseFeat', ['name', 'vocabulary_size', 'embedding_dim']) DenseFeat = namedtuple('DenseFeat', ['name', 'dimension']) dnn_features_columns = [SparseFeat('user_id', train_data['user_id'].nunique(), 8), SparseFeat('movie_id', train_data['movie_id'].nunique(), 8)]
五、构建模型
5.1 输入层
def build_input_layers(dnn_features_columns): dense_input_dict, sparse_input_dict = {}, {} for f in dnn_features_columns: if isinstance(f, SparseFeat): sparse_input_dict[f.name] = Input(shape=(1), name=f.name) elif isinstance(f, DenseFeat): dense_input_dict[f.name] = Input(shape=(f.dimension), name=f.name) return dense_input_dict, sparse_input_dict
5.2 Embedding层
def build_embedding_layers(dnn_features_columns, sparse_input_dict, prefix="", is_linear=True): embedding_layers_dict = {} sparse_feature_columns = list(filter(lambda x: isinstance(x, SparseFeat), dnn_features_columns)) if dnn_features_columns else [] if is_linear: for f in sparse_feature_columns: embedding_layers_dict[f.name] = Embedding(f.vocabulary_size + 1, 1, name= prefix + '_1d_emb_' + + f.name) else: for f in sparse_feature_columns: embedding_layers_dict[f.name] = Embedding(f.vocabulary_size + 1, f.embedding_dim, name=prefix + '_kd_emb_' + f.name) return embedding_layers_dict
5.3 GML
def build_gml_layers(gml_user_embedding, gml_movie_embedding): return Multiply()([gml_user_embedding, gml_movie_embedding])
5.4 MLP
def build_mlp_layers(mlp_input, units=(32, 16)): for out_dim in units: mlp_input = Dense(out_dim)(mlp_input) return mlp_input
5.5 输出层
def bulid_output_layers(concat_output): return Dense(1)(concat_output)
5.6 构建模型
def NCF(dnn_features_columns): # 1. 获取字典输入层，键为列名，值为对应的Input _, sparse_input_dict = build_input_layers(dnn_features_columns) # 2. 获取真实输入层，使用列表存储每个列的Input input_layers = list(sparse_input_dict.values()) # 3. 将SparseFeature进行Embedding，有两路，分别是GML和MLP embedding_gml_dict = build_embedding_layers(dnn_features_columns, sparse_input_dict, prefix="GML", is_linear=False) embedding_mlp_dict = build_embedding_layers(dnn_features_columns, sparse_input_dict, prefix="MLP", is_linear=False) # 4. 将Embedding后的特征进行展开，因为Embedding后为(?,1,8) gml_user_embedding = Flatten()(embedding_gml_dict['user_id'](sparse_input_dict['user_id'])) gml_movie_embedding = Flatten()(embedding_gml_dict['movie_id'](sparse_input_dict['movie_id'])) mlp_user_embedding = Flatten()(embedding_mlp_dict['user_id'](sparse_input_dict['user_id'])) mlp_movie_embedding = Flatten()(embedding_mlp_dict['movie_id'](sparse_input_dict['movie_id'])) # 5. 进行GML，就是展开后的特征进行内积 gml_output = build_gml_layers(gml_user_embedding, gml_movie_embedding) # gml_output = tf.multiply(gml_movie_embedding, gml_user_embedding) # gml_output = Multiply()([gml_user_embedding, gml_movie_embedding]) # 6. 进行MLP，将特征进行连接，传入MLP层 mlp_input = Concatenate(axis=1)([mlp_user_embedding, mlp_movie_embedding]) mlp_output = build_mlp_layers(mlp_input, (32, 16)) # 7. 将GML和MLP层的输出进行连接 concat_output = Concatenate(axis=1)([gml_output, mlp_output]) # 8.传入到输出层中，获取评分 output_layers = bulid_output_layers(concat_output) # 构建模型 model = Model(input_layers, output_layers) return model
六、运转模型
history = NCF(dnn_features_columns) # 编译模型 history.compile(optimizer="adam", loss="mse", metrics=['mae']) # 训练数据做成字典，与输入层做对应 train_model_input = {name: train_data[name] for name in ['user_id', 'movie_id']} history.fit(train_model_input, train_data['label'].values, batch_size=128, epochs=2, validation_split=0.2)
# 绘制网络结构图 plot_model(history,show_shapes=True)
